%0 Conference Proceedings
%T Feature Based Sentiment Analysis for Evaluating the Mobile Pedagogical Affordances of Apps
%+ University of Technology Sydney (UTS)
%A Bano, Muneera
%A Zowghi, Didar
%A Kearney, Matthew
%Z Part 2: Innovative Practices with Learning Technologies
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 11th IFIP World Conference on Computers in Education (WCCE)
%C Dublin, Ireland
%Y Arthur Tatnall
%Y Mary Webb
%I Springer International Publishing
%3 Tomorrow's Learning: Involving Everyone. Learning with and about Technologies and Computing
%V AICT-515
%P 281-291
%8 2017-07-03
%D 2017
%R 10.1007/978-3-319-74310-3_30
%K Mobile learning
%K Sentiment analysis
%K m-learning pedagogies
%Z Computer Science [cs]Conference papers
%X The launch of millions of apps has made it challenging for teachers to select the most suitable educational app to support students’ learning. Several evaluation frameworks have been proposed in the research literature to assist teachers in selecting the right apps for their needs. This paper presents preliminary results of an innovative technique for evaluating educational mobile apps by analysing the feedback of past app users through the lens of a mobile pedagogical perspective. We have utilized a sentiment analysis tool to assess the opinions of the app users through the lens of the criteria offered by a rigorous mobile learning pedagogical framework highlighting the learners’ experience of Personalization, Authenticity and Collaboration (iPAC). The investigation has provided initial confirmation of the powerful utility of the feature based sentiment analysis technique for evaluating the mobile pedagogical affordances of learning apps.
%G English
%Z TC 3
%2 https://inria.hal.science/hal-01762857/document
%2 https://inria.hal.science/hal-01762857/file/463502_1_En_30_Chapter.pdf
%L hal-01762857
%U https://inria.hal.science/hal-01762857
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC3
%~ IFIP-AICT-515
%~ IFIP-WCCE